01. You are analyzing a dataset by using Azure Machine Learning Studio. You need to generate a statistical summary that contains the p-value and the unique count for each feature column.
Which two modules can you use?
Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
a) Computer Linear Correlation
b) Export Count Table
c) Execute Python Script
d) Convert to Indicator Values
e) Summarize Data
02. You use Azure Machine Learning designer to create a training pipeline for a regression model. You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values. What should you do?
a) Clone the training pipeline.
b) Create a batch inference pipeline from the training pipeline.
c) Create a real-time inference pipeline from the training pipeline.
d) Replace the dataset in the training pipeline with an Enter Data Manually module.
03. You are building a regression model for estimating the number of calls during an event. You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain?
Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
a) The label data must be a negative value.
b) The label data must be whole numbers.
c) The label data must be non-discrete.
d) The label data must be a positive value.
e) The label data can be positive or negative.
04. You retrain an existing model. You need to register the new version of a model while keeping the current version of the model in the registry. What should you do?
a) Register a model with a different name from the existing model and a custom property named version with the value 2.
b) Register the model with the same name as the existing model.
c) Save the new model in the default datastore with the same name as the existing model. Do not register the new model.
d) Delete the existing model and register the new one with the same name.
05. How many data scientists can work on a single compute instance that has 8 cores and 56 GB of RAM?
a) Up to five.
b) Up to two.
c) Only one.
d) As many as they want, as long as they don't deplete the compute resources.
06. You plan to provision an Azure Machine Learning Basic edition workspace for a data science project. You need to identify the tasks you will be able to perform in the workspace.
Which three tasks will you be able to perform?
Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
a) Create a Compute Instance and use it to run code in Jupyter notebooks.
b) Create an Azure Kubernetes Service (AKS) inference cluster.
c) Use the designer to train a model by dragging and dropping pre-defined modules.
d) Create a tabular dataset that supports versioning.
e) Use the Automated Machine Learning user interface to train a model.
07. You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.
You need to configure the Tune Model Hyperparameters module. Which two values should you use?
Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
a) Number of hidden nodes
b) Learning Rate
c) The type of the normalizer
d) Number of learning iterations
e) Hidden layer specification
08. You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the DLVM to support CUDA. What should you implement?
a) Solid State Drives (SSD)
b) Computer Processing Unit (CPU) speed increase by using overclocking
c) Graphic Processing Unit (GPU)
d) High Random Access Memory (RAM) configuration
e) Intel Software Extensions (Intel SGX) technology
09. You train a machine learning model. You must deploy the model as a real-time inference service for testing.
The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.
Which compute target should you use?
a) Azure Container Instance (ACI)
b) attached Azure Databricks cluster
c) Azure Kubernetes Service (AKS) inference cluster
d) Azure Machine Learning compute cluster
10. You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch. You need to select a pre-configured DSVM to support the frameworks. What should you create?
a) Data Science Virtual Machine for Windows 2012
b) Data Science Virtual Machine for Linux (CentOS)
c) Geo AI Data Science Virtual Machine with ArcGIS
d) Data Science Virtual Machine for Windows 2016
e) Data Science Virtual Machine for Linux (Ubuntu)
The purpose of this Sample Question Set is to provide you with information about the Designing and Implementing a Data Science Solution on Microsoft Azure exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the DP-100 certification test. To get familiar with real exam environment, we suggest you try our Sample Microsoft Designing and Implementing a Data Science Solution on Azure Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual Microsoft Certified - Azure Data Scientist Associate certification exam.